ArticleScientific reports2024
Vegetation index and livestock practices as predictors of malaria transmission in Nigeria.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 9 citations in OpenAlex.
- Pf41 serological profiling reveals potential malaria exposure in low transmission setting in the Menoreh Hills, Central Java, Indonesia.BMC microbiology · 2026Article
- Identifying age-common and age-specific factors of Plasmodium infection in Nigerian children under five: Application of a cluster-aware multistage selection framework to the 2018 Nigeria Demographic and Health Survey.PLOS global public health · 2026Article
- Bayesian hierarchical spatial ecological model assessing the nexus of vegetation index, insecticide-treated bed-nets and livestock cattle density on lymphatic filariasis transmission.BMC public health · 2025Article
- Modelling the impact of climatic and environmental variables on malaria incidence in Tanzania: Implications for achieving the WHO's 2030 Targets.PLOS global public health · 2025Article
- Waning success: a 2013-2022 spatial and temporal trend analysis of malaria in Ethiopia.Infectious diseases of poverty · 2024Article
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4 authors at 4 institutions in 1 country.
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Abstract
Nigeria is the most malaria-endemic country in the world. Vegetation and livestock practices have been linked to malaria transmission but little is known about these in Nigeria. The study aimed to evaluate the influence of vegetation and livestock as predictors of malaria transmission in Nigeria. Secondary data obtained from the Nigerian Demographic and Health Survey's Geospatial Covariate Datasets Manual were used for the analysis. The survey was carried out successfully in 1389 clusters of thirty (30) households each using a two-stage stratified random sampling design. Hierarchical beta regression models were used to model the associations between malaria incidence, enhanced vegetation index (EVI), and livestock practices. The correlation coefficients for vegetation index and livestock-related variables ranged from - 0.063 to 0.074 and varied significantly with the incidence of malaria in Nigeria (P < 0.001). The model showed vegetation index, livestock goats, and sheep as positive predictors of malaria transmission. Conversely, livestock chicken and pigs were observed to reduce the risk of malaria. The study recommends the need to take into account local differences in transmission when developing malaria early warning systems that utilize environmental and livestock predictors.
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